Spatial patterning among savanna trees in highresolution, spatially extensive data

A. Carla Staver, Gregory P. Asner, Ignacio Rodriguez-Iturbe, Simon Asher Levin, Izak P.J. Smit

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

In savannas, predicting how vegetation varies is a longstanding challenge. Spatial patterning in vegetation may structure that variability, mediated by spatial interactions, including competition and facilitation. Here, we use unique high-resolution, spatially extensive data of tree distributions in an African savanna, derived from airborne Light Detection and Ranging (LiDAR), to examine tree-clustering patterns. We show that tree cluster sizes were governed by power laws over two to three orders of magnitude in spatial scale and that the parameters on their distributions were invariant with respect to underlying environment. Concluding that some universal process governs spatial patterns in tree distributions may be premature. However, we can say that, although the tree layer may look unpredictable locally, at scales relevant to prediction in, e.g., global vegetation models, vegetation is instead strongly structured by regular statistical distributions.

Original languageEnglish (US)
Pages (from-to)10681-10685
Number of pages5
JournalProceedings of the National Academy of Sciences of the United States of America
Volume166
Issue number22
DOIs
StatePublished - 2019

All Science Journal Classification (ASJC) codes

  • General

Keywords

  • Heterogeneity
  • LiDAR
  • Savanna
  • Spatial pattern

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